Language input system for mobile devices

ABSTRACT

A language system facilitates entry of an input string into a mobile device (e.g., cellular phones, PDAs, pagers, etc.) using discrete keys on a keypad, such as a 10-key keypad. The keys have associated letters of an alphabet (e.g., an English alphabet). The key input is representative of one or more phonetic characters (e.g., Chinese Pinyin). Based on this input string, the language system derives the most likely corresponding language characters (e.g., Chinese Hanzi) intended by the user. The language system uses multiple different search engines and language models to aid in deriving the most probable characters. When the language system recognizes possible language characters, the mobile device displays the possible language characters for user selection. The available choices are indexed by specifically chosen selection keys that represent letters of the alphabet that do not commonly follow the phonetic characters already entered. Thus, if the user presses a selection key used to index the language characters, the language system understands that action as a selection of the language character. Alternatively, if the user presses a non-selection key, the language system understands that action as requesting further input. In this manner, the system adopts a modeless entry methodology that eliminates conventional mode switching between input and selection operations.

RELATED APPLICATIONS

[0001] This non-provisional utility application claims priority to theprovisional application number 60/240,584 entitled “Pinyin Input Methodin Mobile Devices and Application of Class-Based Dynamic Dictionary inChinese Pinyin Input System,” filed on Oct. 13, 2000 by Zheng Chen, FengZhang, Rui Yang, Kai-Fu Lee, Mingjing Li and JianFeng Gao, and commonlyassigned to the assignee of the present invention.

TECHNICAL FIELD

[0002] This invention relates to systems and methods for inputting aphonetic text (e.g., Chinese Pinyin) into a mobile device and convertingthe phonetic text to a language text (e.g., Chinese Hanzi).

BACKGROUND

[0003] Character-based languages (e.g., Chinese, Japanese, Korean, etc.)have thousands of characters, making it difficult for users to enter theintended characters or words into computers or electronic devices.Language specific keyboards do not exist for the simple fact that it ispractically impossible to build a keyboard to support separate keys forso many different characters.

[0004] Accordingly, users typically employ a small character-setkeyboard (e.g., an English QWERTY keyboard) to enter phonetic text andthen word processing software converts the phonetic text to theappropriate language text of a character-based language. “Phonetic text”represents the sounds made when speaking a given language, whereas the“language text” represents the actual written characters as they appearin the text. In the Chinese language, for example, Pinyin is an exampleof phonetic text and Hanzi is an example of the language text.Typically, the set of characters needed to express the phonetic text ismuch smaller than the character set used to express the language text.

[0005] While the entry of phonetic text is difficult on smallcharacter-set keyboards, the problem is exacerbated when moving tomobile devices that are commonly equipped with input mechanisms withfewer keys. For instance, a cellular phone commonly has only eight keysto represent all 26 English letters. As a result, attempting to enterphonetic text, such as Chinese Pinyin, into a cellular phone using onlya few keys can be very confusing. From a language processingperspective, the problem is twofold. First, an input system mustrecognize with confidence one or more possible syllables ofapproximately 406 syllables from a string of numbers entered using eightkeys 2-9 on a common 10-key keypad. Inputting, for example, the singleletter “z” might require a user to type “9999” in some products.Entering two or three characters without error in an effort to enter anintended syllable can therefore pose problems. It is noted that thisproblem also exists for input of English characters on mobile phone.

[0006] Second, the system must map the recognized syllable candidates toone or more than 6,000 common Chinese characters. Syllable to characterconversion is a very difficult process, even for large-scale computerswith substantial processing capabilities. The problem is made moredifficult in the context of a limited-resource computing environment,such as a mobile phone.

[0007] Given these problems, there is a continuing need for new methodsthat allow a user to enter phonetic characters (e.g., Pinyin) into amobile device with as few keys as possible, and then automaticallychoose the most likely language character (e.g., Hanzi character) thatthe user intended.

SUMMARY

[0008] A language system facilitates entry of an input string into amobile device (e.g., cellular phones, PDAs, pagers, etc.) using discretekeys on a keypad, such as a 10-key keypad. The keys have associatedletters of an alphabet (e.g., an English alphabet). The key input isrepresentative of one or more phonetic characters (e.g., ChinesePinyin).

[0009] Based on this input string, the language system derives the mostlikely corresponding language characters (e.g., Chinese Hanzi) intendedby the user. The language system uses multiple different search enginesand language models to aid in deriving the most probable characters. Thelanguage system includes a sentence-based search engine and an N-gramlanguage model that statistically derives language characters based onthe phonetic characters and their context within the sentence. Thelanguage system also includes a direct key-based search engine thatgenerates the language characters based on a key sequence entered on thekeypad in lieu of converting the phonetic characters to the languagecharacters. Surname and first name language models are also used todetect proper surnames and first names in the input string.

[0010] When the language system recognizes possible language characters,the mobile device displays the possible language characters for userselection. The available choices are indexed by specifically chosenselection keys that represent letters of the alphabet that do notcommonly follow the phonetic characters already entered. Thus, if theuser presses a selection key used to index the language characters, thelanguage system understands that action as a selection of the languagecharacter. Alternatively, if the user presses a non-selection key, thelanguage system understands that action as requesting further input. Inthis manner, the system adopts a modeless entry methodology thateliminates conventional mode switching between input and selectionoperations.

[0011] Furthermore, the system can be configured to suggest somepossible characters that match the input strings (e.g., when the inputis incomplete) based on the language model. Then, the user can choosethe desired characters from the candidate list instead of typing thecomplete Pinyin strings. This feature is referred to as “autoassociation” and can be combined with the modeless entry methodology toimprove typing speed.

BRIEF DESCRIPTION OF THE DRAWINGS

[0012]FIG. 1 illustrates a mobile phone that is equipped with a languageinput system.

[0013]FIG. 2 is a block diagram of the mobile phone.

[0014]FIG. 3 is a block diagram of the language system implemented inthe mobile phone.

[0015]FIG. 4 is a flow diagram of a process for inputting languagecharacters into the mobile phone.

DETAILED DESCRIPTION

[0016] A system facilitates entry of an input string into a mobiledevice using discrete keys on a keypad that is representative of one ormore phonetic characters. Based on this input string, the system derivesthe most likely corresponding language text intended by the user. Thesystem employs a modeless entry methodology that eliminates conventionalmode switching between input and selection operations.

[0017] The system is described in the context of the Chinese language,although the principles are relevant to other languages. Morespecifically, the system allows a user to enter via the keypad numericstrings that are representative of Pinyin characters (i.e., phonetictext). The system then derives the most likely Hanzi characters (i.e.,language text) from the input string.

[0018] Generally, the mobile device is a compact, lightweightelectronics device. It includes electronics (e.g., microprocessor, ASIC,etc.), memory, and a power source (e.g., battery). The mobile device isequipped with a keypad, such as a 10-key numeric keypad, that utilizesless than 26 keys to represent the 26 letters of the English alphabet.For instance, it is common to use eight keys on the numeric keypad torepresent the 26 English letters. The mobile device is also equippedwith a limited size display, such as an LCD screen.

[0019] For discussion purposes, the mobile device is embodied as acellular phone. However, other mobile devices may be used to implementthe system, including such devices as portable digital assistants(PDAs), handheld computers, pagers, game devices, and the like.

Exemplary Mobile Device

[0020]FIG. 1 shows a mobile phone 100 as one possible implementation ofa mobile device. The phone 100 includes a body 102, an antenna 104, akeypad 106, and a display 108. In this illustration, the keypad 106 isimplemented as a 10-key numeric keypad. Additional control andnavigation keys 110 are positioned above the keypad 106.

[0021] Letters can be entered into the mobile phone 100 via numeric keys2 through 9. For example, letters A, B, and C can be entered using key2, letters D, E, and F can be entered using key 3, and so forth. Thereare different ways to enter a desired letter. One approach is to depressthe key associated with the letter and let the phone 100 (alone or inconjunction with another device(s)) attempt to automatically identifythe intended letter. For example, upon pressing the “5” key, the phone100 attempts to determine which of the letters “J”, “K”, or “L” wasintended, such as based on the preceding inputs.

[0022] Another approach is to depress the same key one or more times toidentify a specific letter associated with the key. For example, toenter the letter “N”, a user presses the “6” key two times. To enter theletter “I”, a user presses the “4” key three times. Yet another approachis to press a number key to identify the set of 3 or 4 letters, and thenpress number keys 1-4 to select the specific letter. For example, toenter the letter “N” with this approach, the user presses the “6” key toidentify the letters “MNO” and then presses the “2” key to select theletter “N” as the second letter in that series.

[0023]FIG. 2 shows the functional components of the phone 100. Itincludes a microprocessor 200, memory 202, display 108, and keypad 106.These components are interconnected via a bus 204. The memory 202includes volatile memory 210 (e.g., RAM, DRAM) and nonvolatile memory212 (e.g., ROM, EEPROM, Flash, etc.).

[0024] The mobile phone 100 is equipped with a language system 220,which is stored in nonvolatile memory 212 and executed on processor 200.The language system facilitates entry of phonetic characters using thekeypad 106. Phonetic characters, such as Chinese Pinyin, enable users toenter a corresponding string of English characters. As one example, theuser enters the Pinyin “ni” by entering the letters “N” and “I” via thekeypad 106. Based on the phonetic-based input string (e.g., Pinyin), thelanguage system 220 automatically selects the most probable languagecharacter (e.g., Hanzi) based on the context of the characters. Thisselection may be made without express mode switching between input andselection.

Language System

[0025]FIG. 3 shows a language system 220 that can be implemented by amobile device, such as phone 100. The language system 220 includes amodeless indexing interface 300, a search engine 302, and a languagemodel 304.

[0026] The modeless indexing interface 300 facilitates seamless entry ofphonetic characters and selection of converted language text. As theuser enters letters via the keypad 106, the modeless indexing interface300 dynamically adjusts the keys to be used either as “input” keys toallow further input of additional phonetic characters or “selection”keys that allow user confirmation of possible converted languagecharacters. The modeless indexing interface 300 includes a user inputinterface 310 and a user selection interface 312 to differentiatebetween the user's input of an additional phonetic text and the user'sconfirmation of an intended converted language character. With theinterface, however, the user need not select a separate mode switchingkey, allowing integrated entry and selection functions.

[0027] The search engine 302 receives the input string from the modelessindexing interface 300 and attempts to identify the most probablelanguage characters given the input string. The search engine includesan auto association module 320 to automatically anticipate possiblelanguage characters after the user enters a partial input string. Thesearch engine 302 also includes a sentence-based search engine 322 todiscern possible language characters in view of the context ofpreviously entered phonetic text. The sentence-based search engine 322tracks phonetic characters as they are entered to glean the user'sintentions as to probable language characters. The search engine 302further includes a direct key-based search engine 324 to search forpossible language characters directly based on key input, rather than onthe phonetic characters represented by the key input.

[0028] The search engine 302 passes the input string to the languagemodel 304, which derives possible language characters based on the inputstring. The language model 304 includes a character-based bigramlanguage model 330 to convert the phonetic character(s) to languagecharacter(s). In the context of Chinese, for a given Pinyin input P, thegoal is to find the most probable Chinese character H, so as to maximizethe probability Pr(H).

[0029] In a bigram model, if a sentence is composed of words W₁, W₂, . .. W_(n), the probability Pr(H) is as follows:

Pr(H)=Pr(W ₁ W ₂ . . . W _(n))≈Pr(W ₁)*Pr(W ₂ |W ₁)*Pr(W ₃ |W ₂)* . . .*Pr(W _(n) |W _(n−1))

[0030] Porting this to the mobile phone input context, for a givenkeypad input D, the goal is to find the most probable Chinese characterH, so as to maximize the probability Pr(H|D).

Pr(H|D)=Pr(H|P)Pr(P|D)

[0031] where P is the possible syllable mapped to the input digits D.

[0032] The language model 304 may also include a unigram surname model332 and a unigram first name model 334 that attempt to identify the mostlikely surnames and first names for a given phonetic word. The languagemodel may optionally include a word list 336 that is a continuous tallyof most frequently used words. The word list might alternatively (oradditionally) include Chinese characters that are added when entered bythe user. If only Chinese characters reside in the word list 336, wordinformation can be obtained from the bigram model. The tradeoff betweenincluding words or characters in the word list depends in part on systemand implementation requirements, such as the amount of memory availablefor the word list. If memory costs are low, a combination of wordinformation and characters will improve entry speed.

[0033] The language model 304 described above resides on the mobiledevice. The language system 220 may further include a server-sidelanguage model 340. With this architecture, the device-based languagemodel 304 may implement a small language model is used to help a userinput Chinese characters without time delay. However, due to processingand memory limitations, the size of the language model is limited. Theserver is not limited in this way, and hence can implement powerfullanguage models, such as N-gram language models that produce moreprecise results. If there is sufficient bandwidth, the mobile device maytransmit the input string to the server side language model 340 via link342 and allow the server to process the input.

Modeless Indexing

[0034] The modeless indexing interface 300 of the language system 220supports a modeless entry methodology that eliminates conventional modeswitching between input and selection operations. With conventionalsystems, the keypad of a mobile phone is used for two functions: inputand selection. As the user presses keys, the conventional systemattempts to identify the intended character. Possible candidatecharacters are indexed using digits 1-9. But the conventional systemcannot automatically distinguish whether the user intends to select oneof the candidates, or enter another letter. For example, suppose a userenters the sequence “64”. The system suggests the following candidatecharacters in association with a corresponding key:

1.

2.

3.

4.

5.

6.

7.

8.

9.

[0035] If the user then enters “6” to yield an input string of “646”,the conventional system will be confused because “6” can be either theselection of “

”, or the input of “646” (“min” or “nin”). To distinguish, conventionalmobile phones are equipped with a mode switch to permit switchingbetween input and selection. If the user desires to select “

”, the user presses the mode switch from input to selection and thenpresses the “6” key. Alternatively, if the user wants to input anothercharacter, the user leaves the phone in the input mode and presses the“6” key. Unfortunately, this mode switching is burdensome and confusingto the user.

[0036] The modeless indexing interface 300 eliminates this problem byimplementing a modeless entry process. FIG. 4 shows the modeless entryprocess 400. This process may be embodied as computer executableinstructions that, when executed, perform the operations illustrated inFIG. 4. The process 400 will be described with additional reference toFIGS. 1-3.

[0037] At block 402, the user input interface 310 permits a user toenter phonetic text via keypad 106. As the user presses discrete keys,the interface 300 receives the key input and passes it onto the searchengine 302 and language model 304 to begin processing the input stringand discern what language character or word the user is attempting toenter. The numbers and/or corresponding phonetic text may also bedisplayed on the display 108 as the user enters them. For instance, ifthe user enters “6” followed by “4”, the mobile phone may depict thenumber series “64” or a possible corresponding phonetic text, such asPinyin “mi” or “ni”.

[0038] At block 404, the language system determines whether the lastentered digit might possibly be the end of the input string. Assuming itis not (i.e., the “no” branch from block 404), the mobile phone presentspossible language characters returned from the search engine 302 andlanguage model 304 (block 406). The available choices are indexed byspecifically chosen keys that represent letters of the alphabet that donot follow the phonetic characters already entered. Continuing the aboveexample, after the user enters “64”, keys “1, 4, 5, 7, 9” are chosen as“selection” keys because the letters associated with digits 4 (GHI), 5is (JKL), and 7 (PQRS) (note that digits 1 and 9 do not have anyassociated letters) would not follow a Pinyin string of “mi” or “ni”.

[0039] Thus, the possible candidates are assigned to the selections anddisplayed as follows:

1.

4.

5.

7.

9.

[0040] The most probable candidate is assigned to the first availableindex key, the second most likely candidate is assigned to the secondavailable index key, and so on. If the user sees a word that he/shewants to input, the user can directly press any one of the keys 1, 4, 5,7, and 9 for immediate selection of the corresponding Chinese character.The remaining numbers 2, 3, 6, and 8 continue being “input” keys becausethey correspond to phonetic characters that still might be entered. Forexample, following entry of “64”, the user may be intending to enter thePinyin text “min” or “nin”. Thus, adding the next digit “6” to form athree digit input of “646” will result in inputting additional phonetictext rather than selection of a converted character.

[0041] It is noted that there may be more candidate language charactersthan can be displayed at any one time. For instance, the search engineand language model may return more than five possible languagecharacters given the input “64”. If the user desires to see morecandidates, the user may activate the scroll up/down control key (seeFIG. 1) to have other candidates displayed. In one implementation,actuation of the scroll control key causes a transition to selectionmode, meaning that the next key input will be a selection. As a result,the language system can output up to nine possible candidates with eachscroll actuations, using digits 1-9. Since the system assumes the useris making a selection, the limited index is not used once the scroll isactivated.

[0042] At block 408, the modeless indexing interface 300 receives thenext key entry. At block 410, the interface 300 determines whether thekey represents an input or selection. In this example, entry of keys 1,4, 5, 7, and 9 represent a selection and keys 2, 3, 5, and 8 representan input. If the user presses an input key (i.e., the “input” branchfrom block 410), the interface 300 receives the new input and adds it tothe existing string (block 402). Alternatively, if the user presses aselection key (i.e., the “selection” branch from block 410), theinterface 300 displays the selected language character. For instance, ifthe user presses the “4” key, the corresponding character

will be displayed.

[0043] With reference again to block 404, if the digit is the finalinput of the sequence (i.e., the “yes” branch from block 404), thelanguage system outputs the most likely character based on the context(block 414). The search engine 302 uses the surrounding words in thesentence and statistically derives the most likely character based onthe user's input and the contextual words.

[0044] Accordingly, depending upon the user's input, the modelessindexing interface 300 dynamically adjusts which keys are used toindexed possible language characters and which keys are used to receivefurther phonetic text. The interface is thus able to differentiatebetween the user's input of an additional phonetic text and the user'sconfirmation of an intended converted language character. With thisinterface, the user need not select a separate mode switching key,thereby seamlessly integrating input and selection. This removes theconfusion inherent in conventional systems and significantly reduces thenumber of keystrokes and entry time.

Direct Selection

[0045] The language system 220 also permits direct searching of possiblecandidates based on the key input, rather than conversion from thephonetic text represented by the key input. In conventional mobiledevices, to input one English letter, a user typically presses a key 1-4times to identify a particular letter. For example, pressing the “6” keytwice, or “66”, correlates to entry of the letter “n”. However, thisinput method becomes somewhat burdensome when entering strings ofcharacters, such as phonetic text. For example, to enter the Pinyin“ni”, a user must type the following sequence “66444”.

[0046] To reduce the number of keystrokes, the direct key-based searchengine 324 of the language system 220 searches for possible languagecharacters (e.g., Hanzi characters) based directly on key input, ratherthan based on the phonetic text (e.g., Pinyin) corresponding to multiplekey entries. As an example, to input “

”, the user enters the shortened key sequence “64” (rather than the longsequence “66444”) and the search engine 302 searches for possible Hanzicharacters that correspond directly to the key sequence “64”. The systemwill also search all possible characters whose Pinyin can be representedby “64”, such as chars whose Pinyin is “ni” or “mi”.

[0047] The direct key-based search engine 324 is trained over time onthe user's input habits and patterns to begin developing statisticalcharacteristics related directly to the key input sequence. Over time,as a user enters a certain sequence that he/she has entered before, thedirect key-based search engine 324 can ascertain the most likelylanguage characters based on the input sequence.

[0048] In one implementation, a character-based language model isapplied to find the most possible Chinese character which responding tothe input digit strings, as follows:

Pr(H|D)=Pr(H|P)Pr(P|D)

[0049] where H is the Chinese Hanzi, P is the Chinese Pinyin, and D isthe input digits.

[0050] While the key-to-character conversion introduces moreuncertainty, it beneficially saves the user numerous keystrokes. Insteadof having to enter “66444” for “ni”, the user might simply enter “64”before being presented with possible Hanzi characters.

Auto-association

[0051] To further reduce the number of keystrokes, the search engine 302employs an auto association module 320 that automatically suggestspossible language characters before a user finishes entering a fullphonetic string. For example, suppose a user types the string “640” toobtain the Hanzi character “

” and the string “636” to produce the Hanzi character “

”. Furthermore, assume that the language system has statisticallydetermined that this user, over time, is more likely intending to enterthe Hanzi character “

” than the Hanzi character “

” when entering the string beginning “6”. Thus, when a user beginstyping “6”, the auto association module 320 identifies that the Hanzicharacter “

” as the most likely candidate and displays this character for possibleselection (e.g., indexing the character with the “1” key) before theuser enters the additional number sequence.

[0052] If the language system accurately predicts the intendedcharacter, the user can simply select the suggested Hanzi character.Alternatively, if the suggestion is not what the user intends (e.g., theuser intends to enter the Hanzi character “

”) the user can continue to press keys to further define the phoneticinput (e.g., enters the sequence “64”). At this junction, the autoassociation module 320 may suggest other possible candidates based onthe 2-digit sequence.

Automatic Selection Based on Context

[0053] The language system 220 employs a sentence-based search engine322. Conventional mobile phones employ more limited word-based searchengines, which are incapable of automatically making decisions as topossible words in the sentence. By employing a sentence-based searchengine 322, the language system 220 is able to consider the context ofthe sentence when statistically deriving the most possible languagecharacters.

[0054] The search engine utilizes an N-gram language model to derivelikely candidates by evaluating the input string in view of one or moreneighboring words. In the described implementation, a bigram languagemodel is used. The bigram language model evaluates a current input inview of the preceding word. Larger language models may be used (e.g.,trigram) if resources permit.

[0055] This context-based statistical analysis is often more accuratethan the single word-based search engines and can be used toautomatically select the most probable language character. A user'sinteraction confirming or denying the automatic selection can also beused to adjust the statistical analysis, thereby improving the accuracyof the system's decision.

[0056] When the user does not find the desired Chinese characters in thecandidate list, he/she can hand over the selection to the search engine.The search engine will choose the most possible words based on thecontext. For example, the function of key ‘0’ on the keypad can bedefined as a search initiation button to hand over the selection job tothe search engine. If the user wants to input “

”, he/she simply types “9404826” followed by the search initiation key“0” and the system automatically outputs “

”. There is no direct interaction between the user and the searchengine.

Client-Server System

[0057] The language system 220 described above employs a residentlanguage model 304 to offer contextual statistics that are useful inanticipating the characters intended by the user. To further improve theaccuracy of selecting possible language characters, the language system220 may employ a more powerful language model that does not reside onthe mobile device. As shown in FIG. 3, the language system 220 mayinclude a nonresident language model 340 implemented at a remote serverthat is communicatively coupled to the mobile device.

[0058] With this architecture, the mobile device functions as an inputmodule to receive the user's input string of numbers (or phonetic text).The language model may then transmit this input to the server-sidelanguage model 340 via link 342. The input string may be sent over isits raw form, or following some local processing by the residentlanguage model 304. The nonresident language model 340 can then performmore powerful statistical analysis to derive possible languagecharacters. The statistical results, or a list of language characters(ranked or unranked), may be returned to the mobile device forpresentation to the user. If there is sufficient bandwidth, the mobiledevice and server can be synchronized and the system will automaticallylearn and adapt to the user.

Conclusion

[0059] Although the invention has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the invention defined in the appended claims is not necessarilylimited to the specific features or acts described. Rather, the specificfeatures and acts are disclosed as exemplary forms of implementing theclaimed invention.

1. A mobile device, comprising: a keypad of number keys, the number keyshaving associated letters; a language system to receive an input stringentered via the keypad that is representative of one or more phoneticcharacters and generate likely language characters based on the inputstring; a display to present the likely language characters for userselection; and the language system being configured to facilitate inputof the input string and selection of a language character withoutswitching modes between input and selection.
 2. A mobile device asrecited in claim 1, wherein the phonetic characters are Chinese Pinyinand the language characters are Chinese Hanzi.
 3. A mobile device asrecited in claim 1, wherein the likely language characters are presentedon the display in an index that associates selection keys of the keypadwith the language characters so that user entry of a selection keyresults in a selection of a corresponding language character and userentry of a non-selection key results in further input.
 4. A mobiledevice as recited in claim 1, wherein the likely language characters arepresented on the display in an index that associates selection keys ofthe keypad with the language characters, the selection keys beingselected based on whether the letters associated therewith follow thephonetic characters already entered.
 5. A mobile device as recited inclaim 1, wherein the language system includes an association module thatautomatically presents the language characters as the user depressesindividual keys.
 6. A mobile device as recited in claim 1, wherein thelanguage system includes a sentence-based search engine to derive thelanguage characters based on context of the input string within one ormore words of a common sentence.
 7. A mobile device as recited in claim1, wherein the language system includes a language model tostatistically derive the language characters.
 8. A mobile device asrecited in claim 1, wherein the language system includes acharacter-based bigram language model and a word-based N-gram languagemodel, where N>2.
 9. A mobile device as recited in claim 1, wherein thelanguage system converts the phonetic characters to the languagecharacters.
 10. A mobile device as recited in claim 1, wherein thelanguage system includes a direct key-based search engine that generatesthe language characters based on a key sequence entered on the keypad inlieu of converting the phonetic characters to the language characters.11. A mobile device as recited in claim 1, wherein the language systemincludes.
 12. A mobile device as recited in claim 1, wherein thelanguage system includes a first name model to detect first names in theinput string.
 13. A mobile device as recited in claim 1, wherein thelanguage system comprises: a first name model to detect first names inthe input string; a surname model to detect surnames in the inputstring; and a character-based bigram language model.
 14. A mobile deviceas recited in claim 1, wherein the language system comprises: a residentlanguage model residing on the mobile device to statistically derive thelanguage characters using a first statistical language model; and anonresident language model residing on a remote server, communicativelycoupled to the mobile device, to statistically derive the languagecharacters using a second statistical language model.
 15. A mobiledevice as recited in claim 1, further comprising a scroll control key topresent other likely language characters.
 16. A mobile device as recitedin claim 1, embodied as a mobile phone.
 17. A mobile device, comprising:a keypad of number keys, the number keys having associated letters of analphabet; and a direct key-based search engine that generates possiblelanguage characters that are not part of the alphabet based on a keysequence entered on the keypad.
 18. A mobile device as recited in claim17, wherein the alphabet is an English alphabet and the languagecharacters are Chinese Hanzi.
 19. A mobile device as recited in claim17, further comprising an association module that automatically presentsthe language characters as the user depresses individual keys.
 20. Amobile device as recited in claim 17, embodied as a mobile phone.
 21. Amobile device, comprising: a keypad of number keys, the number keyshaving associated letters of an alphabet; an association module thatassociates a key sequence with language characters that are not part ofthe alphabet; and a display to present the possible language charactersas the user depresses individual keys based on the key sequence.
 22. Amobile device as recited in claim 21, wherein the alphabet is an Englishalphabet and the language characters are Chinese Hanzi.
 23. A mobiledevice as recited in claim 21, embodied as a mobile phone.
 24. A mobiledevice, comprising: a keypad of number keys, the number keys havingassociated letters of an alphabet; a language system to receive an inputstring entered via the keypad that is representative of one or morephonetic characters and convert the phonetic characters to languagecharacters that are not part of the alphabet using a statisticallanguage model that utilizes at least one neighboring word in a commonsentence; and a display to present the language characters for userselection.
 25. A mobile device as recited in claim 24, wherein thealphabet is an English alphabet and the language characters are ChineseHanzi.
 26. A mobile device as recited in claim 24, embodied as a mobilephone.
 27. A system comprising: a resident language model residing on amobile device to convert phonetic characters input into the mobiledevice into language characters using a first statistical languagemodel; and a nonresident language model residing on a server remote fromthe mobile device, the nonresident language model being configured toconvert the phonetic characters into the language characters using asecond statistical language model.
 28. A system as recited in claim 27,wherein the first statistical language model is a character-based bigramlanguage model and the second statistical language model is a word-basedN-gram language model, where N>2.
 29. A method comprising: receiving aninput string entered via a keypad; presenting likely language charactersbased on the input string; and facilitating continued entry of the inputstring and selection of a suitable language character without switchingmodes between input and selection.
 30. A method as recited in claim 29,wherein the language characters are Chinese Hanzi.
 31. A method asrecited in claim 29, further comprising indexing the likely characterswhen presented in a manner that associates certain keys of the keypadwith the language characters so that user entry of a certain key resultsin a selection and user entry of a non-certain key results in furtherinput.
 32. A method as recited in claim 29, further comprising:associating key entries with the language characters; and presenting thelikely language characters intended by the user as the user depressesindividual keys.
 33. A method as recited in claim 29, further comprisingderiving the language characters using a context-based statisticallanguage model.
 34. A method as recited in claim 29, further comprisingdetecting surnames in the input string.
 35. A method as recited in claim29, further comprising detecting first names in the input string.
 36. Acomputer-readable medium storing computer-executable instructions that,when executed on a processor, perform the method as recited in claim 29.37. One or more computer-readable media having stored thereon aplurality of instructions that, when executed by one or more processorsof a computer, causes the one or more processors to perform actsincluding: receiving an input string entered via a numeric-based keypadwhere number keys in the keypad have associated letters in an alphabet,the input string being representative of one or more phoneticcharacters; converting the input string of phonetic characters topossible language characters that are not part of the alphabet; andpresenting the language characters using an index that associatesselection keys of the keypad with the language characters, the selectionkeys being chosen based on whether the letters associated with theselection keys are likely to follow the phonetic characters alreadyentered.
 38. One or more computer-readable media as recited in claim 37,wherein the phonetic characters are Chinese Pinyin and the languagecharacters are Chinese Hanzi.
 39. One or more computer-readable media asrecited in claim 37, wherein the plurality of instructions further causethe one or more processors to perform acts including selecting one ofthe selection keys to selection one of the language characters.
 40. Oneor more computer-readable media as recited in claim 37, wherein theplurality of instructions further cause the one or more processors toperform acts including selecting a key that is not a selection key tocontinue the input string.
 41. One or more computer-readable media asrecited in claim 37, wherein the plurality of instructions further causethe one or more processors to perform acts including: associating keyentries with the language characters; and presenting the likely languagecharacters intended by the user as the user depresses individual keys.42. One or more computer-readable media as recited in claim 37, whereinthe plurality of instructions further cause the one or more processorsto perform acts including deriving the language characters using acontext-based statistical language model.
 43. One or morecomputer-readable media as recited in claim 37, wherein the plurality ofinstructions further cause the one or more processors to perform actsincluding detecting surnames in the input string.
 44. One or morecomputer-readable media as recited in claim 37, wherein the plurality ofinstructions further cause the one or more processors to perform actsincluding detecting first names in the input string.
 45. A methodcomprising: facilitating entry of phonetic characters via discrete keysof a keypad; and generating possible language characters intended by theuser based on a key sequence entered on the keypad in lieu of convertingthe phonetic characters to the language characters.
 46. Acomputer-readable medium storing computer-executable instructions that,when executed on a processor, perform the method as recited in claim 45.47. A method comprising: receiving key entries entered via anumeric-based keypad where number keys in the keypad have associatedletters; associating strings of key entries with language charactersthat are different than the letters; and presenting likely languagecharacters intended by the user as the user depresses individual keys.48. A computer-readable medium storing computer-executable instructionsthat, when executed on a processor, perform the method as recited inclaim
 47. 49. A method comprising: receiving an input string entered viaa numeric-based keypad where number keys in the keypad have associatedletters, the input string being representative of one or more phoneticcharacters; converting the input string of phonetic characters topossible language characters based upon a context of at least one wordin a sentence within which the input string is a part; and presentingthe possible language characters for selection by the user.
 50. Acomputer-readable medium storing computer-executable instructions that,when executed on a processor, perform the method as recited in claim 49.51. A method comprising: receiving an input string entered via a keypadon a mobile device; sending the input string to a remote server;generating likely language characters based on the input string at theremote server; and returning the likely language characters to themobile device for display.